How XPipes Solves the Challenge of Synchronizing Large Tables Across Any Database Systems

In today's complex data landscape, enterprises often need to synchronize massive datasets between different database systems - from on-premises Oracle to cloud PostgreSQL, from MongoDB collections to ClickHouse tables, or from legacy SQL Server to modern cloud analytics platforms. Our comprehensive data integration platform is dedicated to providing customers with seamless, high-performance cross-database synchronization capabilities.

XPipes enables real-time data integration between any database systems, including:

1. Universal Database Support: Seamlessly connect and synchronize data between PostgreSQL, MySQL, MongoDB, ClickHouse, Oracle, SQL Server, Redis, Elasticsearch, and many more database systems
2. Intelligent Integration Engine: Using advanced CDC (Change Data Capture) mechanisms, intelligent polling, and database-specific optimization techniques to synchronize data between any source and target database combination in real-time
3. Adaptive Performance Optimization: Automatically adapts synchronization strategies based on the characteristics of both source and target database systems

This universal approach allows users to break down data silos and achieve real-time data consistency across their entire database ecosystem, regardless of the technologies involved.

When synchronizing large tables across different database systems, the challenges multiply due to varying performance characteristics, data types, and operational constraints. How to quickly and accurately transfer massive datasets between any database combination while maintaining system stability is a critical capability of XPipes. This article will introduce our adaptive large table synchronization solution in detail.

Challenges of Traditional Data Synchronization Methods

Traditional large data table synchronization is typically accomplished through the following methods:

Full End-to-End Synchronization

Using a SQL query to export all data in the table, then writing it to the target system. This method is simplest to implement and works well for small data tables, but faces the following problems when handling large tables:

• High source database pressure: When performing a full scan of the entire table, it causes enormous pressure on the source database, especially in high-concurrency production environments. Long-duration read pressure may affect users' online business operations.
• Source database resource exhaustion: To maintain long-duration cursor queries, some databases use transaction mechanisms to temporarily store relevant data changes during the cursor's existence in memory (like Oracle) or temporary tables (like SQL Server). Full table scans affect all records, which causes significant additional resource consumption in the database. If not released for a long time, it will eventually lead to database errors and interrupted synchronization.
• Slow synchronization speed: With huge data volumes, the synchronization process may take hours or even longer.
• Non-interruptible: When exporting data with a single SQL query, once the transmission process is interrupted and the cursor exits, there is no effective mechanism to resume it. The entire process needs to start from the beginning.
• Poor real-time performance: All new data changes need to wait until the full synchronization is complete, which causes the data in the target warehouse to experience overall delays of several hours.

Kafka Transfer

First exporting data to a message queue, then consuming it into the data warehouse. Compared to direct end-to-end synchronization, this method has the following advantages:

• Significantly improves data export speed
• To some extent, alleviates the problem of long-duration SQL query occupation

Although the message queue transfer solution has certain advantages, on one hand, it doesn't fundamentally change the mechanism of reading from the source table, only alleviating the problem; on the other hand, the additional components increase maintenance complexity and operational costs.

These problems limit the application of traditional synchronization methods in real-time scenarios. We will introduce our sharding synchronization solution and the different effects it brings in solving this problem.

XPipes' Adaptive Sharding Synchronization Solution

To solve the complex challenges of cross-database large table synchronization, we designed an intelligent, adaptive sharding solution that automatically optimizes based on the characteristics of both source and target database systems. This solution decomposes large-scale synchronization into multiple optimized smaller operations, dramatically improving performance while maintaining data consistency across any database combination.

Universal Technical Implementation Principles

1. Intelligent Sharding Strategy:
   • Database-Aware Partitioning: Automatically selects optimal sharding strategies based on source database type (range-based for Oracle, hash-based for PostgreSQL, document-based for MongoDB)
   • Adaptive Query Generation: Transforms queries based on database capabilities - "SELECT * FROM TABLE WHERE id BETWEEN ? AND ?" for RDBMS, "db.collection.find({_id: {$gte: ?, $lt: ?}})" for MongoDB
   • Dynamic Shard Sizing: Adjusts shard sizes based on source database performance characteristics and target database ingestion capabilities
2. Cross-Database Parallel Processing:
   • Source-Optimized Reading: Uses database-specific connection pooling and query optimization (prepared statements for RDBMS, cursor batching for MongoDB)
   • Target-Optimized Writing: Leverages target database bulk loading capabilities (COPY for PostgreSQL, bulk inserts for ClickHouse, bulk writes for MongoDB)
   • Intelligent Transformation Pipeline: Handles schema mapping, data type conversions, and format transformations in parallel
3. Universal Checkpoint System:
   • Cross-Database State Tracking: Records completion status using database-agnostic identifiers that work across different primary key types
   • Resumable Operations: Supports resumption regardless of source/target database combination
   • Consistency Verification: Implements checksum validation across different database systems
4. Real-Time Incremental Integration:
   • Multi-Protocol CDC: Supports various change capture mechanisms (binlog for MySQL, WAL for PostgreSQL, oplog for MongoDB, etc.)
   • Shard-Level Real-Time Updates: Enables incremental synchronization per completed shard, regardless of database type
   • Conflict Resolution: Handles cross-database timestamp and versioning differences automatically

Technical Advantages

1. Performance Improvement:
   • Sharding synchronization breaks down a single large-scale data query into multiple small-batch queries that can be executed in parallel, fully utilizing the computational capabilities of the source database, improving data reading speed by approximately 3-5 times.
   • The data integration service uses multi-threading technology to encode and decode transmitted data in parallel, which can improve processing speed several times over.
2. Reduced Impact on Source Database:
   • After breaking down large SQL queries into smaller ones, the existence time of each cursor in the source database is significantly reduced. The additional memory and temporary table resource consumption used by the source database to maintain transactions is also significantly reduced, greatly decreasing the impact of the synchronization process on the source database.
   • Sharding synchronization avoids the problem of long-duration cursor locking, greatly reducing the risk of table locking and data conflicts.
3. Improved Reliability:
   • Each shard's failure can be independently retried, enhancing the stability of synchronization tasks.
   • When transmission is interrupted due to network failures, hardware failures, database failures, or various other reasons, the task can skip already synchronized shards and continue from incomplete shards the next time it starts, without having to restart everything from the beginning. This achieves checkpoint resumption functionality and reduces the overall time required to complete the task.
4. Ensuring Real-time Performance: Traditional synchronization solutions need to wait until all full data is completed before synchronizing new events to ensure complete data consistency. After sharding the table, each shard can begin synchronizing incremental data immediately after completing its full data. This means that as long as the data exists in the target database, it will quickly become up-to-date without waiting for all full data to complete before updating, which improves data real-time performance to some extent.

Real-World Case Studies

Case Study 1: Oracle to ClickHouse Migration

A jewelry company needed to migrate their 1TB historical order table from Oracle to ClickHouse for real-time analytics. Traditional export tools failed due to Oracle's "ORA-01555: snapshot too old" errors during long-running queries.

XPipes Solution:

• Automatically detected Oracle's snapshot limitations and applied range-based sharding
• Decomposed the single large query into 100 smaller range queries
• Used Oracle-specific optimizations (hint-based queries, optimal fetch sizes)
• Applied ClickHouse-specific bulk loading with ReplacingMergeTree optimizations
• Result: Successfully migrated 1TB in 4 hours with zero downtime and immediate real-time CDC

Case Study 2: MongoDB to PostgreSQL Integration

An e-commerce platform needed to synchronize their 500GB product catalog from MongoDB to PostgreSQL for relational analytics while maintaining real-time updates.

XPipes Solution:

• Used MongoDB's ObjectId-based sharding for optimal cursor performance
• Implemented intelligent schema flattening for document-to-relational mapping
• Applied PostgreSQL COPY protocol for maximum ingestion speed
• Enabled MongoDB oplog-based CDC for real-time synchronization
• Result: Initial sync completed in 2 hours, ongoing real-time sync with <5 second latency

Case Study 3: Multi-Database Consolidation

A financial services company needed to consolidate data from Oracle (transactions), MongoDB (customer profiles), and MySQL (product data) into a unified ClickHouse analytics platform.

XPipes Solution:

• Coordinated parallel synchronization across all three source systems
• Applied database-specific optimizations for each source
• Implemented unified schema mapping to ClickHouse
• Enabled real-time CDC from all sources simultaneously
• Result: 2TB total data synchronized in 6 hours, unified real-time analytics across all business domains

Conclusion

XPipes' adaptive large table synchronization capability is a cornerstone for building universal real-time data integration across any database systems. Through intelligent sharding strategies, database-specific optimizations, and parallel processing technology, XPipes successfully solves the performance bottlenecks and reliability issues of traditional synchronization methods while supporting any database combination.

The key innovation is recognizing that different database systems require different optimization approaches, and XPipes automatically adapts its strategy based on the specific characteristics of both source and target systems. This ensures optimal performance regardless of the database technologies involved.

If you are looking for a universal data integration solution that can handle large-scale synchronization across any database systems while maintaining real-time performance, we welcome you to try XPipes and experience the powerful advantages of our adaptive synchronization technology!